Show Notes
A brisk, punchy look at AI-assisted cybersecurity and what a real-world security services business could actually look like—and the red flags to watch.
The security-vs-opportunity tension
- AI coding assistants drive more vulnerabilities; security testing is increasingly valuable.
- The business angle: finding and monetizing those vulnerabilities can be lucrative, with fast-moving sales cycles if you’re positioned right.
A practical model: Pen Test as a Service (PaaS)
- High-ticket, quick-turnaround assessments for mid-market to growth-stage companies.
- Focus on prioritization and remediation plans rather than just finding issues.
- Brand positioning matters: emphasize white-hat, trustworthy security rather than “hacker” vibes.
Who to sell to and why
- Ideal customers: companies with 2–10 million+ run rate (or higher) that can afford and need security testing.
- Create a compelling avatar: not the vibe coder; think risk-aware teams who respond quickly to security findings.
- Sales approach: tests and a clear remediation plan; demonstrate quick ROI and measurable risk reduction.
Architecture and tooling sketch
- Backend: Go for performance and reliability; orchestration layer to run tests.
- AI layer: Verscell AI SDK (for rapid client-side orchestration); use AI to gather context signals (funding, breaches, industry chatter) for outreach and scoping.
- Data sources for outreach: LinkedIn data (Sales Navigator, Amplify Scraper, PhantomBuster).
- Outreach signals: funding rounds, industry incidents, recent breaches, etc.
- Testing toolkit: Wireshark, Charles Proxy; test devices (e.g., iPhone) to probe network paths and APIs.
- Browser agents: Anthropics/AI browser agents canaries to surface insights, though capabilities are still evolving.
- Core mindset: pick the right tool for the job; AI is a tool, not a default must-use solution.
Product/Market/Founder risk framework
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Product risk: Is there a compelling product with a clear aha moment? Can you deliver measurable value?
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Market risk: TAM vs SAM; top-down vs bottom-up sizing; price points and volume; customer willingness to buy.
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Founder risk: Team capabilities (e.g., Golang proficiency); ability to execute end-to-end.
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The triangle: constantly assess product risk, market risk, and founder risk as you design and test the idea.
Practical experiments and takeaways
- Run real-world explorations (e.g., hotel-pentesting scenario) to test the concept and reporting flow.
- Build a testable curriculum for topics the AI should cover during a real engagement; keep prompts focused.
- Use clarifying questions upfront and chain prompts to refine plans and outputs.
Safety, ethics, and guardrails
- AI models tend to please; beware over-promising or enabling misuse.
- Stay within ethical/legal boundaries; frame everything as white-hat defense with clear guardrails.
- Use this space as a learning sandbox, not a how-to for offensive abuse.
Tips for moving forward
- Start with a minimal viable test harness and a few pilot engagements to validate viability.
- Use browser agents as a signal of where the space is going, but don’t over-rely on them yet.
- Be pragmatic: if a non-AI approach works better for a given task, use it.
Questions and prompts strategy
- Prepare clarifying questions before engaging an AI for a scenario.
- Use a two-step prompt approach: first gather context, then build a concrete plan and adapt with follow-ups.
Final takeaway
- The AI cybersecurity space is real and potentially scalable, but success hinges on clear target customers, a disciplined risk framework, and choosing the right tools—AI where it adds value, not by default.
Links
- Vercel AI SDK - TypeScript toolkit for building AI applications
- LinkedIn Sales Navigator - Sales prospecting tool
- PhantomBuster - Automation and data extraction platform
- Wireshark - Network protocol analyzer
- Charles Proxy - HTTP debugging proxy
- Anthropic Claude - AI for browser agent capabilities
- VI AI Community - Community for AI builders